Cauda EVs miRNAs analysis using data from both runs
Data: read counts table from both runs
counts_total_run3 <- read.table(
file = "run3_excerpt_counts/exceRpt_miRNA_ReadCounts.txt",
header = TRUE, sep = "", row.names = 1
)
counts_total_run4 <- read.table(
file = "run4_excerpt_counts_/exceRpt_miRNA_ReadCounts.txt",
header = TRUE, sep = "", row.names = 1
)
colnames(counts_total_run4) <- paste0(colnames(counts_total_run4), "_2")
df <- merge(counts_total_run3, counts_total_run4, by = "row.names")
rownames(df) <- df$Row.names
df <- df[, -1]
knitr::kable(head(df))| MS28F1_sample2 | MS28F1_sample3 | MS28F1_sample4 | MS28F1_sample5 | MS28F1_sample6 | MS28F1_sample7 | MS28F1_sample8 | MS28F1_sample9 | MS28F1_sample11 | MS28F1_sample14 | MS28F1_sample2_2 | MS28F1_sample3_2 | MS28F1_sample4_2 | MS28F1_sample5_2 | MS28F1_sample6_2 | MS28F1_sample7_2 | MS28F1_sample8_2 | MS28F1_sample9_2 | MS28F1_sample11_2 | MS28F1_sample14_2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mmu-let-7a-1 | 1.1666667 | 1.5 | 1.666667 | 1.1666667 | 1.3333333 | 0.000 | 0.000 | 2.000000 | 7.5833333 | 4.1666667 | 2.9166667 | 1.750 | 1.000 | 1.500 | 0.9166667 | 0.8333333 | 1.916667 | 2 | 3.333333 | 4.0000000 |
| mmu-let-7a-1-3p|mmu-let-7c-2-3p | 19.0000000 | 34.0 | 21.500000 | 38.0000000 | 42.0000000 | 14.000 | 26.000 | 42.000000 | 95.0000000 | 78.0000000 | 32.0000000 | 53.000 | 31.000 | 42.000 | 20.0000000 | 32.0000000 | 45.000000 | 65 | 103.500000 | 90.5000000 |
| mmu-let-7a-2 | 0.6666667 | 1.0 | 2.666667 | 0.6666667 | 0.3333333 | 0.000 | 0.000 | 3.166667 | 0.5833333 | 0.6666667 | 2.9166667 | 1.750 | 0.000 | 0.500 | 1.9166667 | 1.3333333 | 2.416667 | 5 | 2.500000 | 0.3333333 |
| mmu-let-7a-5p | 819.5000000 | 2422.5 | 1568.833333 | 2313.3333333 | 1867.6666667 | 1422.833 | 1469.583 | 4181.500000 | 5725.0000000 | 4915.0000000 | 3184.1666667 | 4260.667 | 2186.333 | 4359.667 | 2123.3333333 | 3710.6666667 | 4274.666667 | 5608 | 7622.750000 | 7111.8333333 |
| mmu-let-7b | 0.0000000 | 0.0 | 0.000000 | 0.0000000 | 0.0000000 | 0.000 | 0.250 | 0.000000 | 0.0000000 | 0.0000000 | 0.3333333 | 0.000 | 0.000 | 0.000 | 0.0000000 | 0.0000000 | 0.000000 | 0 | 0.250000 | 0.0000000 |
| mmu-let-7b-3p | 10.0000000 | 7.0 | 9.000000 | 6.0000000 | 10.0000000 | 2.000 | 4.000 | 10.000000 | 18.0000000 | 23.0000000 | 10.0000000 | 7.000 | 10.000 | 17.000 | 0.0000000 | 7.0000000 | 9.000000 | 10 | 22.000000 | 22.0000000 |
Specifying the group & batch while filterByExprs()
counts_total <- df
d <- data.frame(
row.names = colnames(counts_total),
group = c(
"MSUS", "CTRL", "MSUS", "CTRL", "MSUS",
"CTRL", "MSUS", "CTRL", "CTRL", "MSUS",
"MSUS", "CTRL", "MSUS", "CTRL", "MSUS",
"CTRL", "MSUS", "CTRL", "CTRL", "MSUS"
),
batch = c(
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2
)
)
counts_total[is.na(counts_total)] <- 0
dds <- calcNormFactors(DGEList(counts_total), method = "TMM")
dds$samples$group <- c(
"MSUS", "CTRL", "MSUS", "CTRL", "MSUS", "CTRL", "MSUS", "CTRL", "CTRL",
"MSUS", "MSUS", "CTRL", "MSUS", "CTRL", "MSUS", "CTRL", "MSUS", "CTRL",
"CTRL", "MSUS"
)
dds$samples$batch <- c(
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2
)
mm <- model.matrix(~ batch + group, data = d)
mm## (Intercept) batch groupMSUS
## MS28F1_sample2 1 1 1
## MS28F1_sample3 1 1 0
## MS28F1_sample4 1 1 1
## MS28F1_sample5 1 1 0
## MS28F1_sample6 1 1 1
## MS28F1_sample7 1 1 0
## MS28F1_sample8 1 1 1
## MS28F1_sample9 1 1 0
## MS28F1_sample11 1 1 0
## MS28F1_sample14 1 1 1
## MS28F1_sample2_2 1 2 1
## MS28F1_sample3_2 1 2 0
## MS28F1_sample4_2 1 2 1
## MS28F1_sample5_2 1 2 0
## MS28F1_sample6_2 1 2 1
## MS28F1_sample7_2 1 2 0
## MS28F1_sample8_2 1 2 1
## MS28F1_sample9_2 1 2 0
## MS28F1_sample11_2 1 2 0
## MS28F1_sample14_2 1 2 1
## attr(,"assign")
## [1] 0 1 2
## attr(,"contrasts")
## attr(,"contrasts")$group
## [1] "contr.treatment"PCA plots
Samples colored by group and shaped by batched
Samples colored by library sizes and shaped by group
Samples colored by library sizes and shaped by batches
Without RUVSeq:
## (Intercept) dds$samples$batch dds$samples$groupMSUS
## MS28F1_sample2 1 1 1
## MS28F1_sample3 1 1 0
## MS28F1_sample4 1 1 1
## MS28F1_sample5 1 1 0
## MS28F1_sample6 1 1 1
## MS28F1_sample7 1 1 0
## MS28F1_sample8 1 1 1
## MS28F1_sample9 1 1 0
## MS28F1_sample11 1 1 0
## MS28F1_sample14 1 1 1
## MS28F1_sample2_2 1 2 1
## MS28F1_sample3_2 1 2 0
## MS28F1_sample4_2 1 2 1
## MS28F1_sample5_2 1 2 0
## MS28F1_sample6_2 1 2 1
## MS28F1_sample7_2 1 2 0
## MS28F1_sample8_2 1 2 1
## MS28F1_sample9_2 1 2 0
## MS28F1_sample11_2 1 2 0
## MS28F1_sample14_2 1 2 1
## attr(,"assign")
## [1] 0 1 2
## attr(,"contrasts")
## attr(,"contrasts")$`dds$samples$group`
## [1] "contr.treatment"dds <- estimateDisp(dds, mm)
fit <- glmLRT(glmFit(dds, mm), coef = "dds$samples$groupMSUS")
res_without_ruvseq <- as.data.frame(topTags(fit, Inf))
head(res_without_ruvseq, n = 20)
With RUVSeq (Only with 1 SV)
re <- RUVSeq::RUVs(en,
cIdx = row.names(en), k = 1, scIdx =
RUVSeq::makeGroups(d$group), isLog = TRUE
)
d$SV1 <- re$W[, 1]
d$SV1## [1] 2.259766 2.329333 2.008852 2.075830 2.135949 2.081968 2.001994 2.516279
## [9] 2.501197 2.496698 2.561585 2.564313 2.319754 2.419298 2.394952 2.324093
## [17] 2.327581 2.732388 2.780277 2.715850## (Intercept) d$SV1 dds$samples$batch dds$samples$groupMSUS
## MS28F1_sample2 1 2.259766 1 1
## MS28F1_sample3 1 2.329333 1 0
## MS28F1_sample4 1 2.008852 1 1
## MS28F1_sample5 1 2.075830 1 0
## MS28F1_sample6 1 2.135949 1 1
## MS28F1_sample7 1 2.081968 1 0
## MS28F1_sample8 1 2.001994 1 1
## MS28F1_sample9 1 2.516279 1 0
## MS28F1_sample11 1 2.501197 1 0
## MS28F1_sample14 1 2.496698 1 1
## MS28F1_sample2_2 1 2.561585 2 1
## MS28F1_sample3_2 1 2.564313 2 0
## MS28F1_sample4_2 1 2.319754 2 1
## MS28F1_sample5_2 1 2.419298 2 0
## MS28F1_sample6_2 1 2.394952 2 1
## MS28F1_sample7_2 1 2.324093 2 0
## MS28F1_sample8_2 1 2.327581 2 1
## MS28F1_sample9_2 1 2.732388 2 0
## MS28F1_sample11_2 1 2.780277 2 0
## MS28F1_sample14_2 1 2.715850 2 1
## attr(,"assign")
## [1] 0 1 2 3
## attr(,"contrasts")
## attr(,"contrasts")$`dds$samples$group`
## [1] "contr.treatment"dds <- estimateDisp(dds, mm)
fit <- glmLRT(glmFit(dds, mm), coef = "dds$samples$groupMSUS")
res_with_ruvseq1 <- as.data.frame(topTags(fit, Inf))
head(res_with_ruvseq1, n = 20)
PCA plot with 1 SV
Samples colored by group and shaped by batched
Samples colored by library sizes and shaped by group
With RUVSeq (Only with 2 SVs)
re <- RUVSeq::RUVs(en,
cIdx = row.names(en), k = 2,
scIdx = RUVSeq::makeGroups(d$group), isLog = TRUE
)
### add the variable (suppose I have only one) to the colData dataframe:
d$SV1 <- re$W[, 1]
d$SV2 <- re$W[, 2]
mm <- model.matrix(~ d$SV1 + d$SV2 + dds$samples$group, data = d)
dds <- estimateDisp(dds, mm)
fit <- glmLRT(glmFit(dds, mm), coef = "dds$samples$groupMSUS")
res_with_ruvseq2 <- as.data.frame(topTags(fit, Inf))
res_with_ruvseq2$LR <- rownames(res_with_ruvseq2)
write_csv(res_with_ruvseq2, "./output/res_with_ruvseq2.csv")
head(res_with_ruvseq2, n = 20)
PCA plot with 2 SV
Samples colored by group and shaped by batched
Samples colored by library sizes and shaped by group
Trying Limma on the same
metadata <- read_excel("/Users/alshanbayeva/data_analysis_september/metadata_caudaEVs.xlsx")
# apply duplicateCorrelation is two rounds
(design <- model.matrix(~ batch + group, metadata))
vobj_tmp <- voom(dds, design, plot = TRUE)
dupcor <- duplicateCorrelation(vobj_tmp, design, block = metadata$Individual)
# run voom considering the duplicateCorrelation results
# in order to compute more accurate precision weights
# Otherwise, use the results from the first voom run
vobj <- voom(dds, design,
plot = TRUE, block = metadata$Individual,
correlation = dupcor$consensus.correlation
)
# Estimate linear mixed model with a single variance component
# Fit the model for each gene,
dupcor <- duplicateCorrelation(vobj, design, block = metadata$Individual) # here the original vobj_tmp was used instead of vobj
# But this step uses only the genome-wide average for the random effect
fitDupCor <- lmFit(vobj, design, block = metadata$Individual, correlation = dupcor$consensus.correlation)
# Fit Empirical Bayes for moderated t-statistics
fitDupCor <- eBayes(fitDupCor)
dealimma <- topTable(fitDupCor, number = 50)
dealimma
dealimma$P.Value <- rownames(dealimma)
write_csv(dealimma, path = "/Users/alshanbayeva/Desktop/caudaEVs_story_manuscript/deepak/raw_data_excerpt/the_final_code_for_submission/dealimma_2runs_miRNAsonly.csv")
# EnhancedVolcano(dealimma, lab = rownames(dealimma), x = 'F', y = 'adj.P.Val')References
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─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.0.4 (2021-02-15)
os Ubuntu 16.04.7 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate de_DE.UTF-8
ctype de_DE.UTF-8
tz Europe/Zurich
date 2021-06-11
─ Packages ───────────────────────────────────────────────────────────────────
! package * version date lib source
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BiocVersion 3.12.0 2020-04-27 [1] Bioconductor
biomaRt * 2.46.3 2021-02-09 [1] Bioconductor
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bit64 4.0.5 2020-08-30 [1] CRAN (R 4.0.4)
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boot 1.3-27 2021-02-12 [1] CRAN (R 4.0.4)
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callr 3.7.0 2021-04-20 [1] CRAN (R 4.0.4)
caTools 1.18.2 2021-03-28 [1] CRAN (R 4.0.4)
cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.0.4)
cli 2.5.0 2021-04-26 [1] CRAN (R 4.0.4)
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DBI 1.1.1 2021-01-15 [1] CRAN (R 4.0.4)
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dplyr * 1.0.5 2021-03-05 [1] CRAN (R 4.0.4)
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estimability 1.3 2018-02-11 [1] CRAN (R 4.0.4)
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extrafont 0.17 2014-12-08 [1] CRAN (R 4.0.4)
extrafontdb 1.0 2012-06-11 [1] CRAN (R 4.0.4)
fansi 0.4.2 2021-01-15 [1] CRAN (R 4.0.4)
farver 2.1.0 2021-02-28 [1] CRAN (R 4.0.4)
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P ── Loaded and on-disk path mismatch.